Remote sensing images have been widely used in various aspects, and will be used further as remote sensing image recognition technologies develop further. In applications of remote sensing, information is collected without directly contacting a related target, and the collected information can be interpreted, classified and recognized. With the use of a remote sensing technology, a great quantity of earth observation data can be acquired rapidly, dynamically, and accurately.
As a hub for marine transportation, port plays an extremely important role and is therefore received more and more attentions, becoming an important research direction in marine transportation traffic planning. In the establishment and planning of a port, port data should be collected first, that is, the various ground objects in a port and their positions should be acquired, a logistics warehouse behind a storage yard is an important ground object in a port, and moreover, logistics warehouses are also crucial for a port.
However, it is somewhat difficult to recognize, based on a remote sensing image, a warehouse in the rear of a port, in the prior art, for example, a method of extracting an image of a logistics warehouse behind a storage yard in a port, which is disclosed in Patent Application No. 201610847354.9, includes: (1) applying a lee sigma edge extraction algorithm to a waveband of a remote sensing image, the algorithm using a specific edge filter to create two independent edge images: a bright-edged image and a dim-edged image, from the original image; (2) carrying out a multi-scale segmentation for the bright-edged image and the dim-edged image together with the remote sensing image to obtain an image object; (3) classifying the ones of the obtained image objects having a big blue waveband ratio into a class A, and removing, using a brightness mean feature, the ones in the class A having a relative low brightness mean from the class A; (4) and removing the objects smaller than a specified threshold from the class A using the Normalized Difference Vegetation Index (NDVI) to obtain the category of a warehouse with a blue roof. Based on features of data and those of a logistics warehouse behind a storage yard in a port, an image can be extracted accurately at a high processing efficiency.
Taking an overall view of the foregoing technical solution, actually existing problems and the currently widely used technical solutions, the following major defects are found:
(1) first, no special method is currently available for the remote sensing reorganization of a logistics warehouse in the rear of a port, because existing methods are applicable to recognize other ground objects and incapable of accurately recognizing a warehouse in a port according to features of the warehouse in the port, moreover, because of the lack of pertinence, the processing of remotely sensed big data is low in efficiency;
(2) second, most of existing data processing methods are based on the direct extraction of a remote sensing image, the biggest defect of this extraction mode is heave original data processing workload, and some undesired data or data out of this scope are usually taken into consideration during this calculation process, thus further increasing the complicity of data processing; and
(3) last, in an existing data processing process, most of feature processing operations are based on spectral features, although full-color remote sensing images have been developed, spectral feature is still disadvantaged in insufficient spectral information, making it necessary to conduct an advanced computation and an interpolation operation for an approximation recovery during a recognition process, however, this process usually triggers a correction algorithm, thus, to obtain a recognized feature that is close to reality, a large amount of calculation needs to be executed, furthermore, an algorithm correction is circulated during this process, leading to a larger computation load.